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Quantitative data is one of the most common methods used for the analysis of data in psychology research. The main reason quantitative data is used by researchers is that it allows researchers to use statistics to identify whether a hypothesis should be accepted or rejected. It is the most scientific approach to research, offering fast and reliable results when performed correctly.
Quantitative data excel in areas where knowledge is being developed, whereas qualitative tends to excel in unknown territories.
Quantitative data is fast, scientific, and easier to analyse, freepik.com/pch.vector
Quantitative data is essentially numerical data that is used in research to summarise, describe, evaluate and analyse datasets.
There are many reasons psychologists use quantitative data in research. Some of these reasons are:
In psychology, research data needs to be interpreted for it to be of use. When this is reported researchers usually use tables, graphs and charts to illustrate what the data is showing.
Some quantitative data examples of charts and graphs used to illustrate data are:
The chart or graph that a researcher uses is determined by what the researcher wishes to describe and illustrate regarding the data. This will be explained in further detail with illustrative examples!
Frequency tables are used by researchers to show the frequency of a group. This table is usually used in psychology research to describe the characteristics of a data set.
The frequency table below shows the number of people who completed a test in each age group.
Age | Frequency | Interpretation |
20 | 40 | 40 people who completed the test were aged 20 |
21 | 27 | 27 people who completed the test were aged 21 |
22 | 33 | 33 people who completed the test were aged 22 |
23 | 35 | 35 people who completed the test were aged 23 |
24 | 52 | 52 people who completed the test were aged 24 |
25 | 49 | 49 people who completed the test were aged 25 |
26 | 30 | 30 people who completed the test were aged 26 |
27 | 26 | 26 people who completed the test were aged 27 |
28 | 30 | 30 people who completed the test were aged 28 |
29 | 50 | 50 people who completed the test were aged 29 |
30 | 56 | 56 people who completed the test were aged 30 |
Bar charts and histograms are both forms of a graph that is used to illustrate frequency tables. This can make it easier for readers to visualise and understand the characteristics of the data.
The data from the quantitative data table example has been used to plot onto a bar chart. This is shown in the graph below.
A bar chart can reveal information about data immediately, Sharon Thind, StudySmarter Originals
The bar chart illustrates that the most people who completed the test were aged 30 and the least were aged 27. When displaying quantitative data in a bar chart and any other graph, it is important that the researcher in their report:
The same data has been used to create a histogram. This is shown in the graph below. Histograms display continuous data.
Histograms are used for continuous data, Sharon Thind, StudySmarter Originals
This shows that 26-31 has the highest frequency density and both 38-43 and 44-49 have the lowest.
Bar charts and histograms essentially have the same purpose of illustrating data from frequency tables. However, the type that the researcher may use is determined by the number of frequencies that the data is measuring.
Data that is measuring the frequency of many variables are likely to use histograms to illustrate this. This is because it groups the data. A bar chart would be inappropriate as there would be too many bars and it may make it difficult to interpret the graph.
Scatter plots are illustrative quantitive data examples. A common inferential statistic used in psychology is correlations. Researchers use scatter plots to illustrate the findings from a correlational test. How a scatter plot looks can be used to:
In a hypothetical psychology report, the psychologist added a scatterplot to illustrate whether there is an association between the age of participants and the number of people who completed the test. This is shown in the example below (the frequency table data was used to plot this).
Scatterplots are great for seeing correlations, Sharon Thind, StudySmarter Originals
From the scatter plot, the quantitative data can be interpreted as there is no relationship/association between the frequency of reporting and the age of participants.
Let's now discuss the advantages and disadvantages of quantitative data!
The advantages of quantitative data are:
The disadvantages of quantitative data are:
Qualitative data is considered by many as holistic whereas, quantitative is not often considered holistic. This is because qualitative data provides detailed information which allows researchers to get an in-depth understanding of phenomenons.
When carrying out research on quantitative data, the data needs to be carried out on a large enough sample to make a conclusion. In addition to this, statistical tests need to be carried out on the data to make a conclusion.
If a list consists of numerical data then it is quantitative however, if it is non-numerical data then it would be classed as qualitative data.
Data collection can be quantitative or qualitative. Quantitative data examples of data collection are questionnaires consisting of close-ended questions, experiments, and fixed-response surveys. However, questionnaires consisting of open-ended questions are qualitative.
Qualitative data is detailed, non-numerical data. Whereas quantitative data is the opposite, numerical data that usually provide a description/ analysis of the summary of results.
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